Some digital maps are generated using machine learning to identify target objects, such as roads in an image. Current applications may rely on known georeferenced road geometry to seed the machine learning process. The georeferenced road geometry may be overlaid on an image to identify road pixels for training. However, in many areas, such as developing countries, the georeferenced road geometry may be poor in quality, coverage, and/or placement. An example of poor map road coverage is provided in FIG. 3. In this example Addis Ababa is depicted in an aerial image, and due to poor road network coverage in this area, only the main roads are depicted. In instances in which there is poor road network coverage or imprecise placement of roads, the map geometry may not correspond to the actual roads of the aerial image.